LSR-Diff: A Diffusion Model Synthesizing Level Set Representations for Reliable Segmentation of Medical Images With...

Accurate boundary segmentation is critical for high-stakes applications such as disease diagnosis, yet remains challenging due to complex topology, boundary ambiguity, and annotation uncertainty. Diffusion Probabilistic Models (DPMs) generate multiple masks with inherent uncertainty, enhancing boundary delineation compared to deterministic models. However, most existing DPM based segmentation approaches learn discrete binary masks, conflicting with the continuous diffusion process and leading to hard-to-learn degradation during noise addition. Moreover, the prevailing approach of averaging stochastic predictions and applying a fixed threshold disregards structural consistency, often leading to imprecise boundaries, isolated artifacts, and holes. To address these challenges, we propose the $L$ evel $S$ et $R$ epresentation $D$ iffusion model (LSR-Diff), which incorporates a diffusion model with a hybrid mask representation to better capture boundary information, and a novel strategy $E$ nsemble $A$ ggregation via Level $S$ et $E$ volution (EASE) to merge prediction candidates while respecting structural information. The hybrid representation takes advantage of both discrete binary masks and continuous implicit masks, with an intermediate representation to ensure a smooth transition. The EASE module guided by ambiguity estimation and anatomical structure then refines boundary topology, preventing arbitrary mask assembly during the aggregation of stochastic predictions. We conduct extensive experiments across various clinical applications including multiple modalities and tissues, showing that LSR-Diff achieves competitive overall performance and improved edge quality and topology accuracy on the tested tasks.